Quantitative MRI Biomarkers of Stereotactic Radiotherapy Outcome in Brain Metastasis

Abstract

About 20–40% of cancer patients develop brain metastases, causing significant morbidity and mortality. Stereotactic radiation treatment is an established option that delivers high dose radiation to the target while sparing the surrounding normal tissue. However, up to 20% of metastatic brain tumours progress despite stereotactic treatment, and it can take months before it is evident on follow-up imaging. An early predictor of radiation therapy outcome in terms of tumour local failure (LF) is crucial, and can facilitate treatment adjustments or allow for early salvage treatment. In this study, an MR-based radiomics framework was proposed to derive and investigate quantitative MRI (qMRI) biomarkers for the outcome of LF in brain metastasis patients treated with hypo-fractionated stereotactic radiation therapy (SRT). The qMRI biomarkers were constructed through a multi-step feature extraction/reduction/selection framework using the conventional MR imaging data acquired from 100 patients (133 lesions), and were applied in conjunction with machine learning techniques for outcome prediction and risk assessment. The results indicated that the majority of the features in the optimal qMRI biomarkers characterize the heterogeneity in the surrounding regions of tumour including edema and tumour/lesion margins. The optimal qMRI biomarker consisted of five features that predict the outcome of LF with an area under the curve (AUC) of 0.79, and a cross-validated sensitivity and specificity of 81% and 79%, respectively. The Kaplan-Meier analyses showed a statistically significant difference in local control (p-value < 0.0001) and overall survival (p = 0.01). Findings from this study are a step towards using qMRI for early prediction of local failure in brain metastasis patients treated with SRT. This may facilitate early adjustments in treatment, such as surgical resection or salvage radiation, that can potentially improve treatment outcomes. Investigations on larger cohorts of patients are, however, required for further validation of the technique.

Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon

“…Advances in machine learning allow for data to be obtained from radiographic imaging (i.e. Radiomics) and digital pathology to enhance diagnosis and predict genomic biomarkers (49)(50)(51)(52)(53). These data will likely lead to the ability of predicting tumor subtype and prognosis prior to surgical resection.…”

Computer Tomography Radiomics-Based Nomogram in the Survival Prediction for Brain Metastases From Non-Small Cell Lung Cancer Underwent Whole Brain Radiotherapy

“…Karami et al. found it was possible to use MRI based radiomics features to predict local failure early for BM patients treated with SRT ( 20 ). Huang et al.…”

“…Karami et al. also investigated that feasibility of using quantitative MRI (qMRI) biomarkers to predict the outcome of local failure for BM patients treated with SRT ( 20 ). Huang et al.…”

Glioma consensus contouring recommendations from a MR-Linac International Consortium Research Group and evaluation of a CT-MRI and MRI-only workflow

“…Further, MRI-based treatment delivery workflow allows direct visualization of normal tissues and tumor-related changes that cannot be adequately appreciated on CT to prompt anatomical based adaptations throughout the course of therapy. Most importantly, an MR-Linac introduces the potential for functional image acquisition such as diffusion, chemical exchange saturation transfer (CEST), perfusion, and other quantitative MRI (qMRI) biomarkers to facilitate early outcome prediction and thereby individualized patient selection for treatment modifications [31][32][33][34][35][36]. It cannot be underscored enough that consistent contouring approaches for all target and OAR structures in glioma is critical in an adaptive strategy based on MR-guided radiotherapy, facilitates radiomics in glioma research [37], and It is recognized that several limitations exist within the current study.…”